Ranking manufacturing processes from the quality management perspective in the automotive industry

The aim of this study is to propose a fuzzy decision‐making model to rank manufacturing processes from the quality management perspective in the automotive industry. This paper proposes a model for improving quality management through the assessment and ranking of manufacturing subprocesses with respect to key performance indicators (KPIs). The developed model, supported with the fuzzy extended ELECTRE III, allows for the determination of subprocesses' rank. An illustrative example indicates that the proposed model could be very useful in everyday business operations as total quality management asset. The model can handle all uncertain and vague input data by applying the theory of fuzzy sets. The research also suggests different managerial implications because it provides an adequate tool for overall quality improvement. The number of treated KPIs is relatively high, so ELECTRE III method gives an advantage over other multicriteria analysis methods because it embraces less subjective thinking and demands slightly less experts' knowledge during the process of decision making and assessment.     

Evaluating the quality of airport service using the fuzzy multi‐criteria decision‐making method: a case study of Taiwanese airports

In today's highly competitive air transportation environment, the quality of airport service is an important issue. In this paper, we present a fuzzy multi‐criteria decision‐making (MCDM) method for the evaluation of the quality of airport service. The data are collected from the passengers who depart from two international airports in Taiwan. The fuzzy MCDM model is defuzzied using the inverse function arithmetic representation method. The results show that the average quality of the Taoyuan International Airport service is better than that of the Kaohsiung International Airport service. This paper also analyses the quality of airport service using the fuzzy expert system to improve the performance of airport service. The analysis results show that the Kaohsiung International Airport and the Taoyuan International Airports' improvement efforts should be concentrated on seven and five specific service items, respectively. The findings of this paper are to help the decision‐makers and the managers of airports improve the quality of airport service.     

犹豫模糊软集

犹豫模糊集是对模糊集的一种推广,它是一类关于域中每个元素所含隶属度的集合,常应用于群决策中,但由于其本身在参数工具上的缺乏使得难于处理不确定数据。为了提高决策的精确性,将软集与犹豫模糊集结合起来,提出犹豫模糊软集的概念,并给出犹豫模糊软集的基本运算法则和性质。     

TRMM rainfall correction over the Andean Plateau using wavelet multi-resolution analysis

Quantifying rainfall from remotely sensed data is crucial for regions where meteorological stations are scarce. This might be one of the only options for analysing rainfall patterns at different temporal and spatial scales in data-scarce environments, particularly in developing countries. The Tropical Rainfall Measuring Mission (TRMM) provides rainfall estimation over the tropics. Rainfall estimates from the TRMM satellite exhibit inaccuracies over topographically complex regions, thus warranting suitable corrections. Multi-resolution analysis (MRA) was applied to improve TRMM 3B42 daily rainfall estimation at 19 meteorological stations located over the Andean Plateau. The detailed signal from each meteorological station was added to the trend signal of each TRMM data cell. Comparing raw and corrected TRMM with gauged rainfall revealed that wavelet-based correction of TRMM 3B42 on average improved several metrics: entropy difference (15.45?1.32), determination coefficient (0.07?0.92), bias (0.68?1.01) and relative mean absolute error (RMAE, 0.86?0.59). The entropy difference of corrected TRMM and gauged rainfall was less than 5%, even when TRMM correction was performed with noise from a station located up to 565 km away from the TRMM cell. This entropy difference corresponded to an average bias of less than 10% in the rainfall estimation.     

A simulation tool for managing environmental flows in regulated rivers

Environmental flows provide river flow regimes to restore and conserve aquatic ecosystems, creating considerably different demands compared to conventional water extraction. With increasing incorporation of environmental flows in water planning worldwide, governments require decision support tools to manage these flows in regulated rivers. We developed the Environmental Water Allocation Simulator with Hydrology (eWASH), a fast, flexible and user-friendly scenario-based hydrological modelling tool, supporting environmental flow management decisions for single- or multi-reservoir systems. Environmental flow demands and management rules are easily specified via the graphical user interface, and batch processing functions aid in uncertainty assessment. eWASH modelled main processes of complex regulated rivers and the tool is widely applicable. We calibrated eWASH for the Gwydir and Macquarie Rivers of Australia's Murray–Darling Basin. Modelled monthly environmental flow allocations exhibited Nash–Sutcliffe efficiencies of 0.55 for the Gwydir and 0.72 for the Macquarie catchments respectively when validated.     

A fuzzy soft flood alarm model

A wide range of hydrological analyses for flood, water resources, water quality, ecological studies, etc., require reliable quantification of rainfall inputs. This work illustrates a fuzzy analysis that has the capability to simulate the unknown relations between a set of meteorological and hydrological parameters. A fuzzy approach to flood alarm prediction based on the fuzzy soft set theory is applied to five selected sites of Kerala, India to predict potential flood.     

A multi-model approach for long-term runoff modeling using rainfall forecasts

This work presents the development of a rainfall-runoff model for the Iguaçu River basin in the south of Brazil. The model was developed to support the operational planning of hydroelectric power plants and is intended to compute natural flow predictions based on meteorological rain forecasts. A recurrent fuzzy system model was employed, with parameters estimated by a genetic algorithm using observed rainfall as input. This work presents the recurrent fuzzy model within a multi-model approach, where the input data are furnished as an envelope, resulting in a prediction envelope that has demonstrated the ability to produce robust results.     

An operational tool to quality control 2D radar reflectivity data for assimilation in COSMO-DE

An operational tool has been designed to enhance the quality of 2D radar reflectivity data for assimilation in COSMO-DE within the German Weather Service (DWD). This article describes the operational algorithms including their testing, the creation of local and composite quality-index fields and their application to improve data assimilation. In the first step, algorithms have been developed and tested to define and identify some of the most severe errors in radar data: corrupt images, low-reflectivity phenomena, which occur under special meteorological conditions, spokes, rings and clutter remnants/speckles. The algorithms use simple but effective tests based on statistical and textural characteristics of spurious signals. The results are stored in an 8-bit quality-index field created concurrently with the radar data at each radar station. It contains quality flags for each individual range bin plus some header information on the overall quality of the underlying data set. The independent coding of error bits enables a differentiated a posteriori decision on whether a range bin is to be used for a given application. These local quality index fields are then used to create a radar precipitation composite, accompanied by a quality-index composite, covering Germany. This tool has now been applied operationally throughout the German radar network. As a result, the radar data quality in data assimilation could be increased.     

基于Hadoop的GA-BP算法在降水预测中的应用

针对如何从海量的气象数据中挖掘出有用的知识,并提高气象预报的准确度,提出了在Hadoop平台上构建基于遗传神经网络算法的天气预报方法.该方法采用遗传算法与神经网络算法相结合,避免了传统算法容易陷入局部最优的问题,并以天津市13个台站1951–2006年的地面气候资料日值数据为基础,建立了遗传神经网络预测模型,最后以降雨量等级为决策属性进行了实验.结果表明,该方法对所有降水等级的预测准确率都要优于传统的神经网络算法,对于降水等级R₀的预测精度最高,达到了87%,不仅可以有效的处理海量气象数据,同时具有较高的预测精准度和良好的扩展性,为天气预报提拱了一种全新的思路和方法.     

地面气象观测站运行监测与控制系统设计

地面气象观测站是一种重要的气象探测设施,由主采集器、分采集器、各种传感器以及供电系统等构成,能够自动观测气温、湿度、气压、风速、风向、雨量等地面气象观测要素;为提升地面气象观测站运行状态监测及时性和丰富程度,实现远程电源控制,研究并设计一套地面气象观测站运行监测与控制系统;系统由站端控制器以及上位机软件组成,以树莓派为核心完成站端控制器硬件设计开发,基于python语言完成上位机软件以及控制器程序设计开发;对系统可靠性、功耗进行测试;测试结果表明：系统对自动站运行状态的成功获取率在99.5%以上,并对运行状态异常具有较好的识别能力;控制器平均值为3.9 W,接入常见的8要素地面气象观测站功耗占比为5.6%,电池供电情况下对蓄电池续航时间影响不大,但在低功耗气象站中使用时应考虑更换更大容量的电池;该系统可进一步提升地面气象观测站运行质量,降低维修保障人员工作强度。     

多决策树的模糊积分融合在银行信贷管理系统中的应用

为了提高基于数据挖掘的商业银行信贷管理系统的信贷风险评估水平,将多决策树的Choquet模糊积分融合(MTCFF)模型应用到银行信贷管理系统中。基本思想是采用决策树在已知类型的客户数据上进行挖掘,按照决策树剪枝程度不同形成不同的决策树并产生规则,利用所生成的不同决策树的规则,对未知类型的客户数据进行分类,然后让Choquet模糊积分对多棵决策树的分类结果进行融合,形成最优判断。采用UCI数据库中German客户信用卡数据集进行验证,实验证明Choquet模糊积分的非线性融合效果优于单棵决策树的分类效果,也优于其他线性融合方法,并且Choquet模糊积分要优于Sugeno模糊积分。     

降水时空关联特征可视分析

目的 降水是影响全球气候变化和系统环境的重要因素,面向降水数据开展时空关联分析,对于区域气候特征探索及异常情况监测具有重要的意义。然而,降水时空关联特征的分析是一个复杂且耗时的过程,与气象站点的空间分布以及降水的时间序列密切相关。本文综合考虑降水的时空变化特征,研究和设计面向降水数据时空关联特征分析的可视化系统工具。方法 利用地图和矩阵图呈现降水数据的空间分布和周期变化特征,设计径向盒须图对降水数据的时空变化异常特征进行捕获;通过局部Moran''s I指数的计算和热力图的呈现表达降水的空间相关性,支持用户交互式地探索空间相关性的时序变化特征;利用普通克里金插值模型获得降水空间插值图,并对插值结果的准确性进行可视化评估。结果 以中国安徽省1971-2014年气象观测站长时间序列月降水数据集为例进行分析,实验结果证明本研究可视化交互系统能够直观高效地探索区域降水长时间序列时空变化特征和极端降水情况;有效探究区域降水空间分布模式、不同站点降水信息间空间依赖性和异质性,并快速发现降水奇异点;分析区域不同时间尺度降水气候特征空间变化。结论 系统工具集成便捷的交互模式,支持用户探索式地分析降水数据的时空关联特征,进而有效地探究区域气候变化规律和特征分布关系。基于真实降水数据的实验结果以及降水领域专家的反馈,进一步验证了本文系统工具的有效性和实用性。     

Using WEC-C,a distributed,deterministic catchment model,to simulate hydrologic responses to agricultural clearing

The water and environmental consultants — catchment model is a distributed, deterministic, catchment-scale water flow and solute transport model specifically developed to assess the effects of land uses such as agricultural clearing, forest logging and surface mining on the stream flows and salinities of a catchment. This paper describes its application to Lemon catchment, a small catchment in the low rainfall zone of the Darling Range of Western Australia, which had half its area cleared for agriculture. Specific parameterisation to Lemon catchment was not the aim; rather a parameter set consistent with our present understanding of the behaviour of Darling Range catchments and with the available hydrological data for the catchment. The resultant model was termed a mimic by the authors in that its appearance and behaviour are similar to the original in a way that satisfies the observer, but due to the known limitations of distributed, deterministic models it, like all such models, cannot be considered a true construct of the catchment. It was noted however that this particular model did appear to accurately mimic the catchment's hydrology and that this was achieved while: extensively using published and measured field data to parameterise the model; using fewer calibrated parameters than comparable models; and successfully matching history to groundwater level and stream flow, salinity and salt load data. The authors believe that a workable methodology for the application of distributed, deterministic models was presented and that the utility of these models was placed into correct perspective.     

Generalized intuitionistic fuzzy soft sets with applications in decision-making

The concept of intuitionistic fuzzy soft set (IFSS) arising from intuitionistic fuzzy set (IFS) is generalized by including a parameter reflecting a moderator's opinion about the validity of the information provided. The resulting generalized intuitionistic fuzzy soft set (GIFSS) finds a special role in the decision making applications. It can evaluate the given criteria along with the moderator's assessment of the furnished data. The properties of GIFSS are investigated and the associated relations called generalized intuitionistic fuzzy soft relations (GIFSR) are given. A similarity measure is given to compare two GIFSSs. As this is not applicable to fuzzy numbers, a new score function is devised to compare two intuitionistic fuzzy numbers (IFNs), the components of IFS. The effectiveness of the proposed GIFSS in decision making is demonstrated on four case studies.     

Fuzzy dynamic multi-objective Data Envelopment Analysis model

Data Envelopment Analysis (DEA) is a managerial powerful tool to evaluate the relative efficiency of each decision making unit (DMU). Nowadays, multi-objective DEA models in static environment are an attractive technique for evaluation quantity and quality aspects of performance analysis because there is some weakness in single objective DEA such as one-dimensional performance analysis and also it is important to consider the decision maker(s) preference over the potential adjustments of various inputs and outputs when DEA is employed. In this paper, a fuzzy dynamic multi-objective DEA model is presented in which data are changing sequentially. This paper assesses the performance of the railways using presented model as a numerical example to evaluate the results of the model. Results indicate that the multiple objective program model improves discriminating power of classical DEA models with just one time calculation of the efficiency achievement for all DUMs.     

基于Vague软集的模糊群决策方法研究

运用传统的模糊集进行模糊型群决策时存在一定的困难,而Vague软集是一种处理不确定信息的重要工具。为此,给出了一种加权的Vague软集间相似度定义,并运用Vague软集对备选方案进行评判;在此基础上运用正态云模型进行群体一致性评判,将基于Vague软集的模糊群决策方法应用于供应商选择问题中,结果表明,Vague软集能极大地提高模糊群决策的科学性和有效性。     

Scenario analysis for the role of sanitation infrastructures in integrated urban wastewater management

Traditionally, the sanitation infrastructures of most of the Urban Wastewater Systems (UWSs) have been managed individually, without considering the many relationships among the sewer systems, Wastewater Treatment Plants (WWTPs) and receiving waters. The main objective of WWTP management was to comply with the emission limits, without considering the ecological state of the receiving waters. However, the European Union approved the Water Framework Directive (WFD) in 2000 that changes the conventional practice by introducing the integrated approach concept in the hydraulic infrastructure management. The same Directive also promotes the availability and use of decision support tools for water management, specifically where water resources are becoming increasingly scarce. This paper describes the work conducted in the Besòs catchment (Catalonia, NE of Spain) in order to deal with this European legislation. A study site was selected to develop an integrated model as a support tool for the UWS management. Specifically, two sewer systems, their WWTPs and a reach of the Congost River (a tributary of the Besòs River) have been modelled. The selected software to model flow and water quality were Infoworks CS, GPS-X and Infoworks RS for the sewer systems, WWTPs and stream reach, respectively. Besides these, a specific program was developed to be used as a data transfer interface between software. Once this model integration platform was built, and taking into account the expert knowledge of the managers, several management scenarios were defined including some critical events such as industrial spills, rainfall episodes, inhibition of nitrification, WWTP shutdowns, obstruction of a sewer system conduit and episodes of minimum river flow rates as well as potential control actions such as the implementation of storage tanks or the use of bypasses between sewer systems or WWTPs. All these scenarios were modelled and simulated and the results obtained were then analysed, focusing the attention on the river water quality, with the main objective being to gain relevant knowledge to deal with the tested scenarios.     

The identification and adaptive prediction of urban sewer flows

The input raw material to a waste-water treatment plant exhibits large, and generally poorly quantified, variations with time. In particular, rainfall run-off can cause gross overloading of the treatment processes of the plant. For a proper operational control of the plant, and hence the quality of the receiving river's water, it would be extremely useful to have advance (short-term) estimates of the effluent flow from the sewer network, i.e. the influent to the plant. This paper studies the feasibility of using an on-line adaptive predictor in such a capacity. The procedure is divided into two steps : (i) the parameters of a multiple input/single output time-series model are recursively estimated at each time-step by the method of least squares ; (ii) a forecast of the plant influent flow is then made on the basis of the newly updated prediction model. Results are presented for data from a treatment plant in Stockholm, Sweden. These demonstrate the adaptability of the predictor to unknown changes in the process dynamics when no information is assumed to be available for rainfall events occurring over the urban land surface,     

Web质量模糊评测矩阵的一致性判定

利用模糊层次分析法(FAHP)评测Web资源质量时,模糊逻辑的引入使成对比较矩阵的一致性判别成为难点。为此,设计最大隶属度元素值替代法,分析并改进基于最小二乘法的一致性判定法。依据判别结果对不一致的模糊矩阵进行修正,直到所有模糊矩阵都满足一致性阈值。分析结果表明,2种方法均能保证Web资源质量评测过程的合理性和结果的可靠性,且后者适用性更高。     

twodee-2: A shallow layer model for dense gas dispersion on complex topography

twodee-2 is a Fortran 90 code based on previous code (twodee). It is designed to solve the shallow water equations for fluid depth, depth-averaged horizontal velocities and depth-averaged fluid density. The shallow layer approach used by twodee-2 is a compromise between the complexity of CFD models and the simpler integral models. It can be used for forecasting gas dispersion near the ground and/or for hazard assessment over complex terrains. The inputs to the model are topography, terrain roughness, wind measurements from meteorological stations and gas flow rate from the ground sources. Optionally the model can be coupled with the output of a meteorological processor which generates a zero-divergence wind field incorporating terrain effects. Model outputs are gas concentration, depth-averaged velocity, averaged cloud thickness and dose. The model can be a useful tool for gas hazard assessment by evaluating where and when lethal concentrations for humans and animals can be reached.